This is puzzling on one hand, but it is also widely accepted by linguists when terms such as Old English and Middle English are given.

with regard to combinability, 20 characters were selected for each of the categories.


3) then there is an instruction ' Please select the word represented by the meaning of this picture 'presented on the screen for 1500ms.

they assumed that distractor stimuli could directly affect the stage of activation of phonologically related morpheme units in the formation level of the production network.


Research showed that the communication of social support in online self-help groups was therapeutic to participants.

employing both qualitative and quantitative methods in future studies to scrutinize the communicative behaviors of users in OSGs and provide comprehensive results would prove beneficial.


As shown in Figure 1, sentences where the subject has the controlling feature [+hon] got 4 Since there were no differences among different Non-subject GRs (i.e., AGREETYPE), we collapsed them into a single category "Non-subject" for the analysis of descriptive statistics.

in fact, the scrambled sentences were judged to be numerically worse than the canonical sentences.


The current experimental results are consistent both with the analysis of A-scrambling as movement to SpTP and the hypothesis that HA and PC in Korean can only be controlled by the lower/thematic subject.

the relative impact of WORD ORDER in influencing the acceptability of HA was 0.10 compared to AGREEMENT.


The example analogies given above are all irregular in form.

the L 2 norm delivered better results for their problem.


To conceal means that the liar withholds some information that misleads the listener.

multidimensional analysis can classify linguistic features into dimensions systematically and scientifically.


This may suggest that when people are telling truth, they tend to depict their feelings in more details, because they have really experienced that process.

(2016) recently studies deceptive utterances from the following five aspects: utterance length and specificity, complexity, hedging and uncertainty; comprehensibility; affect.


Multidimensional analysis has the advantage of examining hundreds of linguistic features at a time and classifying them into several dimensions according to the frequency of co-occurrence among these features.

the results of some research are inconsistent with the findings mentioned above (Hauch, Blando ń-Gitlin, Masip & Sporer, 2012).


We experimented with different thresholds and finally settled on minimum one content word and maximum three content words to be extracted between two NEs.

if there are too many content words intervening between two NEs, then it is likely they are not related at all.


a tourist who is looking for some basic information about a place to visit.

we showed that query-based summarizers biased with a language model for a specific object type perform significantly better than standard querybased summarizers without such models.


Other memory-based approaches to the problem of PP attachment can be found in [12] and [22].

table 4 shows the accuracies of systems trained and tested on the WSJ corpus.


We compare the PP attachment module with a statistical full parsing approach [4] and analyze the results.

a shallow approach also has its shortcomings, an important one being that prepositional phrases, which contain important semantic information for interpreting events, are left unattached.


Questions 18 and 20 were not correctly answered by any of the two systems.

lingPipe 5 and Freeling have been used for English and Spanish respectively.


-the answer can be explicitly stated in one of the blog sentences, or a system might have to infer them; therefore, the answer is highly contextual and depends on the texts one is analyzing, the need for extra knowledge on the NEs (i.e.

firstly, we detect the overall sentiment of the blogs and subsequently a distinction between objective and subjective sentences is done.


Furthermore, we created and annotated a set of questions and answers over a multilingual blog collection for English and Spanish.

the first one is the Question Analysis in which the language object of the study is determined using dictionaries with the criterion of selecting the language for which more words are found.


The first one is the Question Analysis in which the language object of the study is determined using dictionaries with the criterion of selecting the language for which more words are found.

the authors would like to thank Paloma Moreda, Hector Llorens, Estela Saquete and Manuel Palomar for evaluating the questions on their QA system.


All the abovementioned features make blogs a valuable source of information that can be exploited for different purposes.

however, we have seen cases in which each of three different consecutive sentences was a separate answer to a question.


Feature Norm like Knowledge Acquisition For our experiments we choose the feature norm obtained by McRae and colleagues [6].

the pattern precision states how precise the selected pattern is in finding the properties in a certain semantic class and it is computed as shown at the end of the section 2.2.


In order to harvest multi-word expressions and to achieve a better generalization across multiple similar sentences we use the following regular expression of a term definition: The abbreviation NPrep denotes a noun preposition and the straightforward abreviations Adv and Adj denote a adverb an an adjective respectively.

this paper introduces a novel method for acquisition of knowledge for taxonomies of concepts from the raw Wikipedia text.


Identifying these three parts can be very useful, as they follow the classic rule of news writing: the first sentence is the most important one.

the way update is managed is very specific to our system, and taking into account its results should distort the pure summarizing results obtained by CB-SEAS.


Our Spanish SO calculator (SO-CAL) is clearly inferior to our English SO-CAL, probably the result of a number of factors, including a small, preliminary dictionary, and a need for additional adaptation to a new language.

similar to our results, Chinese lexicons created by translating English lexicons did not help performance.


The most interesting difference was the fact that verb forms in Spanish provide irrealis information.

original refers to all the 1,600 original versions and Translated to all 1,600 translated versions.


It is very simple to use someone else's work just by replacing any numeric values, in combination with any necessary rewording.

the resulting word does not need to be a real English word.


After a valid root is found, the denite article is determined and then the search hits for the generated plural form(s) are obtained.

after the disambiguation phase, only the verb and the adjective paradigms for gebruind survived' and they are correctly taken to be the nal outcome.


Non-comparable adjectives like amorf (amorphous) have only the base and the base inected form.

a vowel is long if it is doubled (e.g., maan moon), if it is in a vowel combination (lief sweet, dear) or when it is at the end of a syllable (maken to make).


Such adjectives have only a base form and thus, no valid paradigm is found for them.

we search for occurrences of de + root and het + root and return the article with the higher number of search hits.


There are 188 cases of morphological ambiguity.

the corpora have already been parsed with the Alpino parser and many words have been added to the lexicon of the Alpino grammar.


Restricted only by that limited knowledge the FSTs, in analysis mode, identify all possible forms and roots allowed by the word structure.

for almost each adjective three analyses will be delivered boost (angry): *boo, *boos and booz.


Supervised machine learning techniques are still superior to unsupervised machine learning techniques for many NLP tasks.

inflection is a productive, but rather simple (in comparison to languages like Spanish or Finnish) morphological process in Afrikaans, with nine basic categories of inflection, viz.


measured as entropybased on the annotated datathat is inversely related to the voting confidence of the classifier).

contrary to intuition and to results for AL in other areas than language processing, it is the selection of less prototypical instances first that provides the best improvement, both for word frequency and word length.


First, we will explore ways of improving these results.

third, we asked four professional lexicographers to manually assign synonyms to definitions.


Polysemy was defined by the number of definitions assigned to the verb by the TLFi.

the definitions of a synonym associated with a given verb usage (reflexive vs. non-reflexive) were compared only with the definitions of this particular usage.


His experiments on PERSON type factoid questions have not shown a considerable improvement.

from TREC 2006 QA dataset are shown in Table 2.


An empirical evaluation using TREC 2006 QA data set showed significant improvements using our query expansion method.

the same technique was found effective for the ad-hoc retrieval task.


A systematic relationship between the outcomes of extrinsic evaluation and properties of a system's output can indicate directions for improvement in output, leading to improvements in the system's utility in its target setting.

in this work, we used two definitions of informativeness: (i) the number of (clinical) events NE that a text references; (ii) the length of the text in tokens (words) NW.


In what follows, we summarise the main observations, discussing their implications in Section 7.

the significant effect of main target action does suggest that some of the burden is carried by the content selection strategies in the H and C texts, with the human-authored summaries incorporating more information that was relevant to the appropriate actions.


This evaluation is similar to the one relying on manually assigned user-features.

illustration: The content presents an illustration of a concept or a process, either through the use of images, or through diagrams.


In the fine grained evaluation, all four dimensions are considered, and thus we run a four-way classification.

instead of using the user annotations, we use the output automatically predicted by the classifiers.


Moving to State S6 is triggered by the word the, which has neither backward nor forward dependencies; however, it is linked through a chain of dependencies with a future word board.

here, we examine the effect of lookahead features on the supertagger, operator tagger and dependency results.


Instead the raw frequencies are compared directly.

for this method, a bilingual dictionary and a small amount of parallel data for the ME classifier is needed.


Sentences are ranked by this score, and the highest-scoring sentences are selected for the summary.

furthermore, the topic space is much smaller than the original term vector space.


Runs realized changing the specific parameters of Random Indexing as the number of dimensions and the number of training cycles show that the optimum partition realized with the Sub-target vector algorithm using 9 subtargets doest not change significantly (between 0.740 and 0.746 for the Precision and between 0.708 and 0.718 for Recall).

target vectors will be more sensible to typical documents and less sensible to non-typical documents.


The system gives 92.1% recall and 89.7% precision over the test corpus (247 test items) in WD, M1 and M2.

our strategy to obtain the best disambiguation option was to chose first the morphosyntactic disambiguation level, and then we selected the best option for syntactic disambiguation.


we prepared the development set from section 21 of the treebank as in [5].


Compared to [5,6], we also extend the definition of "centroid" from a word to an entity; and target at linking extracted facts instead of sentences.

a high-coherence text has fewer conceptual gaps and thus requires fewer inferences and less prior knowledge, rendering the text easier to understand [1].


Our basic underlying hypothesis is that the salience of an entity e i should be calculated by taking into consideration both its confidence and the confidence of other entities {e j } connected to it, which is inspired by PageRank [13].

we also take into account other events that have already been generated to maximize diversity among the event nodes in a chain and completeness for each event node.


2 The parsing model of MSTParser has the advantage that it can be trained globally and eventually be applied with an exact inference algorithm.

among the heuristics considered were majority votes for constituents and a similarity-based measure for complete trees.


Under these conditions, an analysis having only a few grave conflicts may be preferred by the system against another one with a great number of smaller constraint violations.

in its interaction with external predictors WCDG should typically decide about the alternatives.


In this respect, the increase in the structural precision of the PP attachment seems worth mentioning.

mSTParser as a statistical parser trained on a full corpus becomes a strong competitor for a PP attacher that has been trained on restricted four-tuples input.


This comparison shows that the new examples have a radically different sense distribution than the SensEval data.

we have to keep in mind here that we only added a minimal number of examples (141).


The quality of the lexicon has been evaluated with respect to adjectival tables listed in [13].

this will also allow us to validate the remaining treelex frames and verify performance for individual adjectives.


We consider the propositional constituents in (5) the extraposed subject of the adjective, i.e., in impersonal constructions (the subject is il or ce), OBJ is the subject of ATS adjective.

in such cases, the subject of the adjective can be easily identified as it is indicated by the grammatical function of another argument of the verb: SUJ for ATS, and OBJ for ATO adjectives.


Therefore, no other linguistic observations can help us specify the status of PPs in APs.

this list contains 177 adjectives found only with a basic frame in the corpus and there are 127 adjectives occurring with different frames in text.


However, building large and rich enough predicate models for broad-coverage semantic processing takes a great deal of expensive manual effort involving large research groups during long periods of development.

the original SSI algorithm is very simple and consists of an initialization step and a set of iterative steps.


Again, the the best results are achieved by both FSI and ASI variants on nouns and adjectives, and FSP on verbs.

remember that FSP always uses I and the first senses of the rest of words in P as context for the disambiguation.


Doing so naturally increases all word frequencies, turning low frequency words into high frequency ones.

instead, removing input data would do the job, by turning high frequency words into low frequency ones.


They both, together with a Baseline, have been evaluated in TIMEX3 identification for English and Spanish.

the presented approaches have been tested in tE identification within the previously described corpora and the results have been compared to the original tIMEX3 annotation.


On the one hand, taking into account that same quality results have been obtained for English and Spanish using the same approach, this study will be extended to other languages to confirm if the analyzed hypothesis could be considered multilingual.

due to the fact the presented approach is based on semantic roles and multilingual semantic networks information, it could be valid also for other European languages that share several features at this level.


Although originally intended to provide evidence of how much redundancy should ideally be included in generated anaphoric descriptions, preliminary findings reveal a number of little explored issues that are relevant to both referring expressions generation and interpretation.

this level of variation was deemed necessary to avoid domain and other linguistic effects 5 , and also to prevent the subjects from relying on memory.


Constraintbased systems have the succinct advantage that in principle any modelled property of the target structure -be it linguistic or non-linguistic -can be constrained by the mere addition of appropriate further constraints.

the predictor can only provide dependency scores for word pairs -and not for syntactically more complex units such as phrases or clauses.


The experiments described above lead to the conclusion that we need a hamza module that can take the raw text and restore the hamzas if necessary, before the text is passed to the diacritization system.

the first step towards this problem is a system that reaches a high accuracy in an in-vitro evaluation, i.e.


They reach a WER (for diacritization) of 16.5% on conversational Arabic.

these studies leave the lexical context of words for the most part unexplored.


Abrupt polarity changes between (sub)contexts are boosted by v : for example, a NEG (sub)context followed by a POS one may indicate a shift in perspective or negation.

we believe that access to these rich layers is required for deeper logical sentiment reasoning in the future.


In the near future, we plan to further evaluate the Leffe by comparing the coverage and precision of different deep parsers that rely on the same grammar but using different morphological and syntactic lexicons such as the Leffe.

we would like also to thank the group Gramática del Español from USC, and especially to Guillermo Rojo, M. Paula Santalla and Susana Sotelo, for granting us access to their lexicon.


In order to obtain such behavior, we simply bypass the internal lexicon.

the final result is a morphological and syntactic lexicon with an important coverage in terms of morphological information but a more restricted one in terms of syntactic information.


For example, all verbal lemmas that were not covered by ADESSE or SRG received the following subcategorization frame: <Suj:sn|cln,Obj:(sn|cla)> (transitive verb with optional direct object).

we used a corpus built from a subset of the Spanish part of the Europarl 19 containing approx.


Results of Classifiers 1 and 3 indicate that learning the tasks jointly produces a moderately better performance.

the ranking process customized to semantic dependencies is suboptimal.


Here, we discuss the computational complexity of the learning.

it reserves oversized margin against mistakes of the label elements during learning.


Algorithm 1 shows the expansion to structured output learning with kernel.

when the average cost is used, the utterance position does not change results much.


This yielded 612 pairs, each of which was judged by a linguist 7 as being a pair of false friends, partial cognates, or true cognates.

nakov & Pacovski [31] tried various combinations of these statistics; in their experiments, the bestperforming formula was the following one: now, note that we have the following inequalities: having a high number of co-occurrences #(w bg , w ru ) should increase the probability that the words w bg and w ru are cognates.


Let us consider the cases where R = {data base} and we have the following output lists: O 1 = {data base, data bases}, O 2 = {data bases} et O 3 = {data base, table of content}.

in biology, it was reported that extractors often propose incomplete terms, such as core rna, which are nevertheless kept in a modified form (core rna polymerase) by terminologists [2].


The pattern selection process has been carried out for each system configuration, and evaluated on the test data.

insertion, deletion and substitution) which are required to transform T into H. Each edit operation on two text fragments A and B (denoted as A − → B) has an associated cost (denoted as γ(A − → B)).


For example, in Figure 1, "tree" is encoded by "001", "node" is encoded by "011" and so on.

by using PartialGen, b is grown by appending every word in s into every title in b to make a list of titles of length j.


Section 5 presents our experimental results with discussion about the results.

the output of this step is a tree of titles which is called a table-of-contents.


Lexical forms are even more "suspicious" if present in non-parsable along with forms "cleared" by their presence in parsable ones [9].

it is essential for the formalisms to be compared to various kind of languages and practical tools in order to adapt and extend them.


In practice, this means that we should expect the system to rate an 'average' grade as something else in 2 out of 241 cases (0.83%), a relatively minor error rate that we expect to accommodate in the subsequent (and also classification-driven) stages of the generation process.

within-sentence structuring is performed as follows.


This may in principle seem counter-intuitive, as the textual realisation of one message could hinge on whether others are realised or not, but such dependencies were not observed in our We are interested in two particular aspects of Document Structuring (and which to some extent cover aspects of Microplanning in the standard pipeline NLG architecture in [1] as well): the task of organising the content messages computed in the previous Content Determination stage into sentences, and then organising these sentences in a global rhetorical structure.

meanings could span over entire sentences or even paragraphs.


Table 3 reports the KSMR results for all possible combinations of the probability distributions used for f 3 (Uniform (U) and Geometric (G)) and for f 5 (Uniform (U), Geometric (G), and Poisson (P)).

the expanded hypotheses are stored into a stack data structure which allows the efficient exploration of the search space.


The concept of partial phrase-alignment is similar to the concept of complete phrase alignment described in section 4.

7, a very straightforward technique can be proposed for finding the best phrasealignment of a sentence pair (f , e).


In this paper, we also focus on the IMT approach to CAT.

let us suppose that we are covering the source phrasẽ f ≡"lista de recursos" given by the source positions u ={4,5,6}.


It is also important to look at less-spoken but still prominent languages (Figures 5 and 6).

if we look at some top-priority issues of today -such as health, economy, homeland security, stem-cell research, science teaching -and pressing research questions, such as how to enhance child development and learning and even how to make sense of the huge amount of information with which we deal daily, we can say that future topics will be so complex as to require insights from multiple disciplines.


Node OBJ4 represents the last embedded object clause, which has the trace as an immediate constituent, the infinitive clause aller in our example.

according to the table, there are two kinds of interactions: • Linear interactions: a linear interaction occurs between exactly one positive feature f → v 1 and one negative feature f ← v 2 to combine in a saturated feature f ; in this way, both features become saturated.


(4) as follows: From the last equation, we can see that, if we multiply the values of all attributes by the constant a, a = 0, the classification decision will remain the same (provided that we use no smoothing).

the figure further shows that using 1+q has little effect, i.e., useless attributes are not penalized enough.


In a random partition with k parts (clusters), for each word in a pair the probability for the other word of being in the same cluster is 1/k.

the resulting word vectors will be similar for words that appear in similar contexts.


Each classifier was trained per relation as in the previous experiments, but this time as negative examples we considered only those belonging to the corresponding contingency set.

it is interesting to note that most of these contingency sets involve Part-Whole.


Table 2   Global precision figures P avg @ 1, P avg @ N and MAP (mean average precision) for Experiment Sets 1, 2 and 3 (automatic evaluation) are presented in Tables 3, 4 and 5.

since our approach relies only on minimal linguistic processing, the results presented can be considered a baseline for other methods that try to perform the same task, using additional linguistic information.


From the OpenOffice thesaurus we collected (verb → list of synonyms) mappings for 2,783 verbs, each having 3.83 synonyms in average.

results achieved by combining the cosine distance with the Mutual Information weighting function suggest the low frequency features carry most of the information regarding verb similarity.


Results achieved by combining the cosine distance with the Mutual Information weighting function suggest the low frequency features carry most of the information regarding verb similarity.

automatic methods usually involve a large set of parameters, whose impact on final results is difficult to assess, and thus to optimize.


Size-bounded partitioning, on the other hand, can be used to obtain pre-clusters of manageable sizes, while at the same time committing much less understemming errors.

if we consider longer prefixes, more morphologically related word-forms will end up being assigned to distinct pre-clusters.


We omitted word combinations (get happy), collocations and idioms.

we study Russian and Romanian emotional words.


On the other hand, words which denote abstract concepts as sensations, feelings and emotions keep more sound semantics in their forms (anger, joy, agitation).

on the Russian and Romanian data sets, the applied algorithms performed considerably better than baselines.


Starting in 2000, for non-English speaking regions, the growth has surpassed 3,000 % compared with the over-all growth of 342%.

for example, the sound z is present in Russian words with meaning of amazement; the transliterated sound sh can be found in Russian words representing a kind of stupefaction (there is no absolutely precise translation of these words in English).


Our goal was to build domain-independent rules that do not rely on domain content words and emotional words.

it eliminates the bias introduced by the length of the text.


This method can also be applied to analyze texts which do not explicitly disclose affects such as medical and legal documents.

the authors did not actually analyze the texts or language cues contained in the reviews.


However, our concern is on the accuracy of the taggers instead of their speed and memory requirement.

the SVMM0C0 has been trained with the same data that has been used to train the tnt-based tagger, tag-ger2.


The results show that the dialogue act labelling task can be improved by including the probability distribution of the number of segments.

to corroborate this problem, we calculated the precision, recall and F-measure of the experiments.


The confidence interval for this experiment and confidence interval of the baseline show that the difference between the results given by the models are statistically significant.

the last element P (S c ) is estimated by another gaussian distribution that is computed from all the turns: the mean m Sc and variance σ Sc are computed from all the scores in the training data.


We report in Table 2   The average sentence length in the Europarl corpus is more than double than that in the MultiBerkeley corpus due to the different selection strategy of the sentences.

in general, the two results reflect the different goals of the two approaches: [1] are interested in investigating and adopting unsupervised techniques with poor semantic and syntactic information to automatically annotate a large scale (but noisy) training set and exploit it for semantic role labelling.


If possible, we preferred Italian translations minimizing divergences with English.

as for MultiBerkeley, we could not apply algorithm 1 because it requires the source sentences to be represented as syntactic trees, whereas the English FrameNet corpus has annotation pointing to flat chunks without parsing information.


Afterwards, we present the evaluation and results.

the results are then combined and used to derive a similarity metric between short texts.


The former case involves a rather straightforward mapping: since the ontology includes WordNet identifiers a mapping with Cornetto amounts to the retrieval of WordNet identifiers in the database.

it is reported in [13] that 985 concepts from WordNet 1.6 have been assigned the music label.


What is most interesting, however, is that this required so minimal an investment in new data annotation.

indeed, since manual annotation can be costly and time-consuming, it is important to maximize the effectiveness of annotation efforts.


Secondly, as mentioned earlier, if the target word is infrequent, it will have a low in-degree in the dictionary graph, and this is especially the case for TOEFL synonym questions, which tend to test the participants on a morechallenging vocabulary of relatively low-frequency words.

this phenomenon provided evidence for the adaptability of dictionary-based methods in different domains or cultures.


Given W 0 and P 0 , we now follow this procedure for synonym extraction: 1.

one of the objectives of PbE is to discover such patterns.


This is different from the dictionary graphs in IIE and its variants, which relate a definiendum to all its definientia.

dictionary definition texts, as a special form of corpora, can provide a better "controlled" environment for synonym distribution and thus, it would presumably be easier to find characteristic features specific to synonymy within definition texts.


It is only surpassed by the recall value of random sampling with the lowest ratio of 1:1.

german is morphologically richer than English, for which most of the work has been done, and it possesses grammatical gender.


by including examples from all areas of the search space.

we did optimize the classifiers' parameters.


The supervised keyphrase extraction systems use the extraction model obtained from the training data to classify the candidates into keyphrases and rank them according to their importance in the document.

we collected three publicly available datasets with different properties, which allows comparison of the applicability of keyphrase extraction algorithms to those datasets.


Its features are tuned using a genetic algorithm.

supervised approaches use a corpus of training data to learn a keyphrase extraction model that is able to classify candidates as keyphrases.


The DUC dataset [24] consists of 308 documents from DUC2001 that were manually annotated with at most 10 keyphrases per document by two indexers.

they differ in length and domain (see table 1), and can thus be used to assess different properties of keyphrase extraction algorithms.


For example, if we always extract 10 keyphrases, but a document only has 8 gold keyphrases assigned, then 2 extracted keyphrases will always be wrong.

the total number of selected approximate matchings is 566, as some matchings were included in multiple sets of the random matchings and morphological approximate matching Morph did not always account for 100 approximate matchings per dataset.


Let synsets(w) denote the set of WordNet synsets for a word w, and let CW be the set of candidate words used by S10.

for instance, we do not attempt to extract representations for durative or repetitive events, or actions like escalate or accelerate that change quantities or numerical attributes.


If the events are related to the main category of the article, only knowing the article category is enough.

as the event extraction system uses a supervised model, it is natural to ask whether supervised topic features are better than unsupervised ones.


Unsupervised LDA performs best of all, which indicates that the real distribution in the balanced corpus can provide useful guidance for event extraction, while supervised features might not provide enough information, especially when testing on a balanced corpus.

hunting-related or shooting-contest-related activities should not be tagged as Attack events.


For example, the word "attack" is very likely to represent an Attack event while the word "meet" is not.

2009) to outperform SVMs when extracting tag-specific document snippets, and is competitive with SVMs on a variety of datasets.


Finally, once all arguments have been assigned, the trigger classifier is applied to the potential event mention; if the result is successful, this event mention is reported 3 .


We compare this unsupervised approach to a supervised multi-label text classifier, and show that unsupervised topic modeling can get better results for both collections, and especially for a more balanced collection.

we find that the gains are unevenly spread across different events.


In the entertainment domain, the first negative seed removes the strongest bad rule.

we can cautiously conclude that the underlying minimally supervised bootstrapping approach to IE is not necessarily doomed to failure for domains that do not possess beneficial data sets for learning.


A systematic analysis studies the respective data properties of the three domains including the distribution of the semantic arguments and their combinations.

the second negative seed does not lead to big jump in the performance.


Unfortunately, one is often constrained by the lack of resources, tools or language experts, for instance when dealing with resource-scarce languages.

it usually requires a part-of-speech (POS) tagger and a training corpus annotated with shallow parsing tags.


The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 under Grant Agreement n° 248005 11 .

it is not clear how the absence of any part-of-speech tagger should hamper the development of other natural language processing tools.


The extended lexicon was used in combination with the extended transitions based on trigrams from section 5.3.

tagger (MElt) (Denis and Sagot, 2009) is a conditional sequence maximum entropy POS tagger that uses a set of lexical and context features, which are a superset of the features used by Ratnaparkhi (1996) and toutanova and Manning (2000).


From the error analysis, we can conclude that the added words do not correspond to the words that are needed in the test domain, which means that the HCRC map task corpus data are not similar enough to the CReST data.

the results of the previous sections show that adding information on which taggers trained outof-domain agree is useful for moderately improving tagging accuracy and especially for reestimating transition probabilites.


In addition to components for visual perception and action execution, DIARC consists of five NLU components.

the parsing task is more difficult than in other experiments.


For each MICA elementary tree t j and XTAG elementary tree t i the observation probability matrix (B = [b i, j ]) also contains the probability P(t j |t i ).

as it shows, here also the M-2 gives a better response in compare to the M-1.


it comes at very end of the clause as shown in the example with bold-face: On the contrary, if an adverb is used as a complement of verb then it comes before the main verb, as shown in the following example: The lexical category V has three forms (corresponding to perfective/imperfective aspects and subjunctive mood).

the structure of VPHForm makes sure that we preserve all inflectional forms of the verb.


Evaluating a resource grammar is just like evaluating a software library in general.

cN is a syntactic category, which is used to deal with the modifications of nouns by adjectives, determiners, etc.


For instance, if a sentence or a question sentence is a complement of the verb then it takes a different position in the clause; i.e.

the current systems only seem to provide partial solutions, mainly because of the vocabulary differences (Humayoun and Ranta, 2010).


This covers only three main types of noun phrases, but there are other types of noun phrases as well, i.e.

: the way this rule is implemented may vary from one language to another; as each language may have different word order and/or agreement rules.


To obtain these scores, we multiply, respectively, the sentence values for the Pat-tFreq, CatFreq and CatOcc features by the ObjSim feature value.

for generating such summaries, we took into account the type of information users reflect when writing summaries of this particular domain.


Regarding the readability assessment, Table 5 showed that our approach obtains close results to the human performance in Aker and Gaizauskas (2010b).

to construct them we adopted the dependency relational patterns extraction described by Aker and Gaizauskas (2010a).


Our results indicate that our approach achieves high performance both in ROUGE and manual evaluation.

tS is an especially challenging Natural Language Processing (NLP) task, since the generation of summaries depends on a wide range of issues, such as the summarization input, output or purpose.


The weight w i of each hyperedge is given by the context sensitive discriminative model discussed in section 4.4.

each triple consists of two concepts (or instances of concepts) connected by a relation.


A hypergraph (H) is a generic graph wherein edges can connect any number of vertices and are called hyperedges.

the total number of sentences in the answers is 1862, i.e., 2.596 sentences per an answer.


4 The Apertium linguistic data contains 326 228 entries in the bilingual dictionary, 106 firstlevel rules, 31 second-level rules, and 7 third-level rules for Spanish-English; and 21 593, 169, 79 and 6, respectively, for Breton-French (see section 2.2 for a description of the different rule levels).

the results show that our hybrid approach outperforms both pure RBMt and PBSMt systems in terms of BLEU.


Thus, as soon as the PBSMT system learns reliable information from the parallel corpus, Apertium phrases become useless.

table 5 shows the proportion of RBMtgenerated phrases used to perform each translation.


We focus on alleviating the data sparseness problem suffered by phrase-based statistical machine translation (PBSMT) systems (Koehn, 2010, ch.

the difference is statistically significant only under certain circumstances.


It is always important to highlight that post-edited translations that do not match a reference are not necessarily bad as they could still be valid paraphrases.

when BLEU is computed with multiple references, even though the translations from all systems may differ from what was originally expected (ref 0 ), they can still be valid alternative translations that often match the choices made by other translators (ref 1−17 ).


The code consists of a library that implements the actual text matching, and of a number of source files to demonstrate how to match entities in a text and how to extract the entity information from the NE resource file.

the matching software, after reading and analysing the NE resource file, searches for any of the known entities in multilingual text.


1 We refer to "source" and "target" language for convenience only-our models are symmetric, as will become apparent.

we present a baseline model and several successive improvements, using data from the Uralic language family.


The parameters L(e), or P (e), for every observed event e, are computed from the change in the total code-length-the change that corresponds to the cost of adjoining the new event e to the set of previously observed events E: Combining eqs.

we use the principle of recurrent sound correspondence, as in much of the literature, including the mentioned work, (Kondrak, 2002;Kondrak, 2003) and others.


2 To model also insertions and deletions, we augment both alphabets with the empty symbol, denoted by a dot, and use Σ .

no gold-standard alignment for the Uralic data currently exists, and building one is very costly and slow.


248347, and by the Slovenian Research Agency, grant no.

we use context-dependent cognates because calculat-ing cognates between all lemmata of specific parts of speech proved to be very noisy even on high cognate thresholds and it did not have a positive impact on this task.


Contexts are chosen from each interval in a round robin fashion in order of least entropy from each group.

we hypothesis that if this is the case, an independent measure of a cluster's sentiment will show a high likelihood that a cluster to be either positive or negative in sentiment overall or be a mixture of positive and negative sentiments so that the overall sentiment is neutral.


We perform POS tagging with the Stanford parser (Levy and Manning, 2003).

after analyzing the wrongly classified examples, we found that the Chinese word segmentation errors propagate to sentence boundary detection task.


To detect the topic clause is difficult.

all the Chinese characters in a paragraph are successive (one by one) without word, clause, and sentence boundaries.


Many connectives (such as because or for instance) always signal one specific discourse relation.

on one hand, this feature can detect co-taxonomic pairs such as current-new or risefall (as well as nontaxonomic relations such as accident-injured) whenever these occur very frequently.


Work on explicit (i.e., connective-bearing) relations has emphasized simpler features, such as the syntactic neighbourhood of the connective  or features based on tense and mood of the argument clauses (Miltsakaki et al., 2005).

section 3.1) are improved by the rich set of features.


Ambiguous temporal markers such as after, as or while usually occur with a purely temporal reading, but also with additional non-temporal discourse relations, such as causal and contrastive readings.

the best feature set achieves F-measures of 0.41 (contrast), 0.39 (parallel) and 0.33 (evidence) on these relations, with precision values between 0.33 (evidence) and 0.36 (contrast), and recall values between 0.33 (evidence) and 0.47 (contrast).


We firmly believe that our results in detecting noun compounds and named entities can be fruitfully applied in other higher-level applications as well in e.g.

the keyphrase extractor can still profit from already known NEs: in one case, they can be excluded from the set of keyphrase aspirants while in the other case, they are proper keyword candidates.


no phrase was regarded as a keyphrase aspirant if it occurred only in the References part of an article.

the mwetoolkit basically does not mark MWEs in the raw text, it just extracts noun compounds from the text, i.e.


Demonstrative NPs are mapped to nominal NPs by matching their heads (e.g.

bridging anaphora was not resolved by the system in these experiments (but still included in the evaluation) which might be a reason for the relatively low recall.


Regarding the statistics about the words, it is worth noting that the documents in Romance languages (Spanish and French) have similar characteristics.

in the current society, information plays a crucial role that brings competitive advantages to users, when it is managed correctly.


Briefly, the main features of this approach are: i) redundant information is detected and removed by means of textual entailment; and ii) the Code Quantity Principle (Givón, 1990) is used for accounting relevant information from a cognitive perspective.

in this way, we use LingPipe 3 for English, the illinois Named Entity Tagger 4 (Ratinov and Roth, 2009) for French, the NER for German 5 proposed in (Faruqui and Padó, 2010), and Freeling 6 for Spanish.


News-Gist (Kabadjov et al., 2010) is a multi-lingual summariser that achieves better performance than state-of-the-art approaches.

the top levels were populated by the sentences that appeared in the most languages and the bottom level contained sentences appearing in the least number of languages.


This is done in a straightforward way: the polarity of the aggregate opinion is computed as the average of the polarity of all the opinions from the source on the topic.

extraction/Coreference Resolution Next, our system labels fine-grained opinions with their topic and decide which opinions are on the same topic.


In case of fusing event type information, it turned out that for 5 out of 51 events in our corpus none of the mono-lingual systems was able to assign any type information.

as can be observed, a gain of 4-5% and 8% in precision and recall could be obtained for the extraction of event type and numerical slots respectively.


Therefore, it is very hard for a learning algorithm to precisely determine the relation types.

second, the results of  EM 1 , EM 2 , and EM 3 are the results of our proposed method with three different initializations.


As it is shown, NONE relations have had a great impact on the accuracy of the system.


They report consistent gains on argument classification by combining models based on different similarity metrics.

chunk or Base Phrase: the tokens and POS tags within every base phrase were collapsed into a surface and a POS span, respectively.


We present an approach for Semantic Role Labeling (SRL) using Conditional Random Fields in a joint identification/classification step.

disappointingly our system performs worse than that by Mitsumori et al.


Following, we present the results after analyzing the entire corpus of the Senseval-2 competition.

to do this we use the Equation 2 where we have substituted the variable w (word) with the variable sw i , (sense) where sw i indicates the i-th sense of word w. we convert each RSt in a vector.


Given the nature of the corpora provided for the 1st International Competition on Plagiarism Detection, we cannot apply them to test a speller sys-tem given that the plagiarisms are automatically generated and therefore they do not contain misspellings (Potthast et al., 2009).

there-fore, it would be beneficial to apply a spell corrector over the documents in our corpora, such as the one described in (Gao et al., 2010).


AttP: In the final setting, singleton pronouns are attached to an antecedent.

table 1 shows the results for both baselines.


Entity Instantiations are not considered in the MUC and ACE annotation schemes, which consider relationships between different types of entity, such as those between persons and locations, rather than our groups and instances of entities of the same type.

our work is also related to the problem of bridging anaphora.


This of course explains why there has been an increasing amount of research both on named entity recognition and on relation analysis in the last 20 years (MUC6, 1995;Appelt and Martin, 1999).

the success rate is often above .9 or even .95 F-measure for major categories (person's names, location's names) in newspapers (Collins and Singer, 1999).


In addition, we also test 1%, 5%, 10%, 25% and 50% of the training set.

very few high-level features describing the presence of certain semantic classes or opinion words perform consistently well across different domains.


Sometimes it was not unequivocal to decide whether a multiword unit is a noun compound or a NE.

the CRF+OwnNE+SF row in table 6 represents results achieved when the NEs identified by using the entire Wiki50 as the training dataset functioned as a feature.


For each Persian word w and each PWN synset t, ߠ ௪,௧ is considered as probability of selecting PWN synset t for Persian word w. That is: In order to estimate these parameters we can divide the number of times that a Persian word w occurs with PWN synset t in a Persian tagged corpus to the number of times that a Persian word w appears in that Persian tagged corpus.

table 3: and average number of occurrence with respect the learning maximum changes in probabilities become less than a predefined threshold.


For English, PWN has been used while for Czech, Romanian and Bulgarian WordNets from the BalkaNet project have been used.

that is, we find: In order to maximize ܳሺΘ, Θ ିଵ ሻ subject to constraint has shown in formula (2), we introduce the Lagrange multiplier λ and to find the expression for ߠ ௪,௧ , we should to solve the following equation: Whit solving differential equation 7, we obtain the new value of parameters as follows: in order to calculate new estimation of parameters, according to the formula (8) we must iterate over all possible sense tagged sequences ܶ ଵ for Persian word sequence ݓ ଵ .


Such statistical models that rely on topics will not generalize well over text in previously unseen topics or genre.


we conjecture that N-gram sequences are not robust against domain changes, as N-grams are powerful features for topic categorization (e.g., (Türkoǧlu et al., 2007)).


As shown in Table 1, these categories were not completely disjoint.


As before, the mixture of all features actually decrease the performance.

we should like to point out that this extremely high performance of simple features are attainable only when supplied with sufficiently large amount of data.


It is proved that articles from the selected set are better socially annotated for all tested language, which results in a good weighting indicator of value 42,73.

usually, motivation was approached by classic social sciences methodologies which discuss about where it resides, in the individual by itself or in it while is acting.


Also, one of the cor-relations we noticed was that the more endogamy in terms of inlinks, the less interwiki links it had.

it can include writers and geographic places, music and historical objects.


We use with the dataset from SemEval-1 Task 4 on Classification of Semantic Relations between Nominals (Girju et al., 2009), which is the most popular dataset for our problem; using it allows for a direct comparison to state-of-the-art systems that were evaluated on it.

the ability to recognize semantic relations in text could potentially help many NLP applications.


Instead, we mine the Web to automatically extract patterns (verbs, prepositions and coordinating conjunctions) expressing the relationship between the relation arguments, as well as hypernyms and co-hyponyms of the arguments, which we use in instance-based classifiers.

relational similarity focuses on the relationship between two pairs of words (or nominals, noun phrases), i.e., it asks how similar the relations A:B and C:D are.


This is probably due to the fact that we employ a more sophisticated system for identifying DNIs.

the omission in (8) is lexically specific: the verb arrive allows the GOAL to be unspecified but the verb reach, also a member of the Arriving frame, does not (9).


One can distinguish two views of the use of morphology as a tool for term (or word) analysis.

note that in this paper, we distinguish morphs, elementary linguistic signs (segments), from morphemes, equivalence classes with identical signified and close significants (Mel'čuk, 2006).


As mentioned in Section 2.2, the LA model also leaves some of the features underspecified or assigns default values to them.

as stated above, unlike Alpino, the GG does not have a full form lexicon.


Figure 1 shows a sample of verb table 12.

figure 2 shows a sample of the hierarchy.


They share some features with our approach: they use bootstrapping; they only weakly depend on the language; they are domain independent.

this decrease can be significantly limited via human pattern selection, as can be seen from the performance of the CT. system Overall, the automatic approach proposed here, coupled with a lightweight human post-processing step, generates a good quality pattern lexicon for information extraction.


If we compare the number of the learned rules and the learned instances in Table 3, all four experiments do not differ too much from each other.

among the instances with three arguments, there are two argument combinations where W stands for winners, P for prize names, Y for years and a for areas.


Our data pre-processing on the noisy Wikipedia topic pairs involved using simple regular expressions to filter out most of the irrelevant entities including: characters from other writing systems; temporal and numeric expressions; and punctuation symbols.

a DBN model is formally defined as a pair B 0 , B → where B 0 is a Bayesian network over an initial distribution over states, and B → is a two slice Temporal Bayes Net (2-TBN) (Murphy, 2002).


The classic HMMs in particular have already been applied in detecting transliteration pairs from bilingual text.

we used the same parameter for staying in the X or Y state, another same parameter for moving from either X or Y to the end state, and the same parameter from the substitution state to the X or Y state.


This information can be useful for a subsequent phase of normalization.

numerous research works at the crossroads of NLP and data mining, are focusing on the problem of opinion detection and mining.


It takes the user question as an input and returns a query-tuple representing the question in a compact form.

we use JAPE grammars to specify patterns over annotations.


She suggests several simple heuristics for ranking the candidate trees, two of which will be considered here.

the results are very encouraging on several dimensions: 1. overall parsing performance on the test data for both the np-wsjqb and the np-wsjqblq 500 models is very good 2. adding questions from the NLP-Qt yields a desired increase in performance 3. almost two-thirds of all questions from the test data yield a completely correct parse.


6 For example, Nachttischlampe 'bedside lamp' has both hypernyms Tischlampe 'table lamp' (hypernym distance is 1, i.e., direct hypernym) and Lampe 'lamp' (hypernym distance is 2, i.e., indirect hypernym).

furthermore, the splitting of compounds having more than two constituents, such as Brennstofflagerungsbehälter ('fuel storage container'), which is split into brennen + Stoff + lagern + Behälter cannot be used in this form for determining immediate constituents, since the constituents are not grouped.


pared with the limited database of annotated clauses created by the expert.

is given in figures 3 to 6 with the following parameters: m = 316, N max = 400 and t rel = 0.157 designed to produce M = 8 groups after aggregation, because the two evaluation indexes have interesting value  Classes identified by the expert  Total  arg  descr  dial  expl  inj  nar   Clusters   1  48  30  33  34  15  101  261  2  4  0  2  1  0  0  7  3  0  0  1  1  0  0  2  4  0  0  2  0  0  0  2  5  0  2  2  0  8  0  12  6  2  0  1  0  3  0  6  7  5  0  5  4  0  3  17  8  0  1  0  6  0  2  9  Total  59  33  46  46  26  106  316   Table 4: Cross-counts between unsupervised and manual classification.


Indeed, this text is made of a journal part.

this preliminary work aims at investigating a possible convergence between unsupervised statistical learning on the one hand, and the typological approach developed in particular in the field of French linguistics by Adam (2008) and in language psychology by Bronckart (1997) on the other hand.


The RapidMiner Top Down Clustering operator was chosen as the number of clusters was selected by the operator.

the expected lexicon of CEOs should contain overtly positive language, which is designed to manipulate the public opinion.


The lexicon analysis was the same as for the CEOs i.e.

the clusters, which contained 75% of a single category had their labels propagated and the job titles from the propagated and labelled data were recorded.


The lexicon difference in conjunction with the baseline experiments provides justification for the two-step strategy as it will be possible to identify the role of the quotation maker by his language.

in order to collect as strong as possible quotations, a high precision rule classifier selected quotations with three or more adjectives from one category.


With the success of the communicative and actionoriented approach in the teaching of a second or foreign language (L2), teachers are encouraged to work on authentic texts in order to bring their students in contact with real linguistic data.

in this study, we defined the 7 following variables to assess the efficiency of n-gram models in the context of readability: • The normalized log-probability of every text (normTLProb), which is in keeping with Kate et al.


They aim to develop tools capable of assessing the difficulty of texts for a given population through textual features only (such as the number of letters per word, the number of words per sentence, etc.).

four thresholds were selected for each of the two references: a zero threshold where all nominal structures were considered, a second and a fourth one respectively corresponding to a 30% and 50% precision for our extractor, and an intermediate value as the third threshold.


Faced with these results, one might conclude that MWEs are not as good predictors as the simple complex nominal structures (θ = 0).

there is another textual aspect that is likely to be a good predictor of lexical difficulty for L2 readers: collocations and idioms.


The discriminative capability of higher-order models suffers too much from the smoothing, since the number of unknown n-grams increases proportionally to the model order.

we assume that at beginner or intermediate level, this facilitating effect is likely to be counterbalanced by the fact that the MWEs encountered are (1) mostly unknown to readers and (2) even more difficult to elucidate using the context as their meaning can be non-compositional.


These gaps can in general be filled either by short (unwritten) vowels or by long (written) ones.

10: the third singular masculine and feminine and third dual feminine forms have suffixes which begin with (a) added to them, so Fig.


o m ), its total cost, denoted by C w (P ), is: Let .

for each (T,H) pair there might be many proofs.


We have shown that the system outperforms baselines that are restricted to each of these information sources alone; that is, both structural and similarity information are essential.

then, we partition the participant descriptions for each scenario into sets.


This shows that parsing accuracy has a considerable effect on the overall performance of the system.

the difference between full and base is significant at p < 0.001.


Difficult cases, mostly related to metonymies, were solved in consultation.

the next step must demonstrate that this can be done, without requiring the manual selection of scenarios to ask people about.


Our content organization approach first categorizes questions to determine which schema will better convey the expected communicative goal of the answer for a particular question type and should be used for text planning.

in this experiment, BlogSum achieved a better F-Measure for ROUGE-2 and ROUGE-SU4 compared to OList.


In our approach, schemata help to ensure the global coherence of the summary.

predicate: (6) At Carmax, the price is the Attributive * price and when you want a car you go get one.


In our test, we have developed our own sentence extractor based on question similarity, topic similarity, and subjectivity scores.


The follow-ing two sections describe a new concept, parallel suffix arrays, and how it enables annotations and more powerful search patterns.

although optimizing the implementation's use of RAM is certainly possible, it is quite clear that possibilities to use the described approach in linguistic practice depend on whether servers with sufficiently large RAM are available, and affordable.


Our initial evaluations showed considerable reduction of time needed to create a taxonomy using the tool comparing to manual taxonomy creation.

the hierarchy approximates a directed acyclic graph (DAG).


(2005) that contains pairwise human similarity judgments for 1,225 text pairs.

for example, the text pairs about tool/implement and cemetery/graveyard were consistently said to be synonymous.


The dataset comprises 21 questions, 21 reference answers and 630 student answers.

further datasets are necessary to guide the development of measures along other dimensions such as structure or style.


We analyze the properties of each dataset by means of annotation studies and a critical view on the performance of common similarity measures.

in authorship classification (Holmes, 1998) only style is important.


Of course, checking all cases of plagiarism manually is an unfeasible task.

the external detection phase will make the system more precise in the task of plagiarism detection thanks to the high precision of the external detection techniques.


This lexicon has been created from a mapping between the TimeML event classes and the SIMPLE/CLIPS entries at the ontological level and it is composed by 8,721 lemmas (1,068 for adjectives, 4,614 for nouns, 3,390 for verbs).

the SemEval tempEval-1 and tempEval-2 international evaluation excercises (Verhagen et al., 2007;Verhagen et al., 2010), have provided the NLP community with gold standard resources for comparative evaluations of different systems.


The other two best performing models differ from the basic one for the combination of morphological features and presence of semantic features.

the similarity of the results with respect to the recall is not surprising.


It is worth noticing that the context windows differentiating TIPSemIT FPC5 from TIPSemIT basic do not contribute at all to an improvement in classification, while this feature has a positive impact on event recognition.

the semantic information is not always a necessary and sufficient condition for its classification.


Furthermore, we can associate a particular regular expression for each one of the six possible forms of a verb in this tense.

the form "ceartȃ" is however generated by the grammar, as: we cannot say that the grammar models this alternation.


to these dictionaries, we add some entries related to the sport domain.


The comparison was done with only one reference translation, as we work in a realistic scenario with dynamic domain change (see section 1.)

for English-Romanian, SMT systems are presented in (Cristea, 2009) and (Ignat, 2009), with BLEU results of 0.5464 and 0.3208, respectively.


For Romanian-German our result overtake the system presented in (Ignat, 2009).

test 3) is identical, the differences in BLEU and tER score can be explained only through lexical and syntactical variation across test-sets.


A more general formulation does not limit the methods described, though a small set decreases runtime.

the work presented in this paper aims towards an automatic detection of beneficial struc- Senator John Green .


This does not make the domain specific development unnecessary (as the application of feature selection still needs the development of features for a domain), but helps to find templates to improve the results.

to previous work, this paper addresses the question how to select meaningful skip edges automatically from a set of possibilities.


In this section, we create a key set by human assessment as our gold standard: we mixed up the machine translation, human corrections, and reference for one original sentence and 5 annotators in Chinese-English, and 3 in Spanish-English, were asked to assess the sentences scores from 1 to 5, where 1 corresponds to poor and 5 corresponds to perfect translation.

it is another kind of cross-validation, where the quality of a correction is based on other corrections from the same user.


For governor and dependent, we use the semantic similarity mentioned in section 4.3.2.

we checked the data and found that the overall language model score for translated Spanish is better than reference, which means for Spanish, the fluency is not the big problem.


condition, drug, result) and modifiers (e.g.

there is no such annotated available corpus.


Estimating Probabilities for Action Maps: In obtaining frequency estimates for synonyms, we require these phrases to co-occcur with instances where a known verb appears.

note however, that we do not explicitly use any predicates or logical structures; these are implicit in the visual schema.


But clearly more work is needed to be able to approach verbs that are not directly based on motion.

we remove some of the topmost frequent words "the" in this analysis where they appear as part of a phrase.


During association, we remove units that are very frequent in general discourse, assuming these to be non-relevant to this context.

a number of approaches have tried to construct such term-meaning associations from sensorimotor data (Steels and Kaplan, 2002;Gorniak and Roy, 2004;Roy and Pentland, 2002;Oates et al., 2000).


As for the individual features, 'MFV' proves to be the most successful on its own, thus, the changes in the verb list are beneficial.

the stems of decision and decide do not coincide).


As our results indicate, the distance is small between the source and the target domain in the case of light verb constructions since similar results can be achieved on the two domains if domain-specific solutions are employed.

as represented in Table 3, syntax clearly helps in identifying light verb constructions: on average, it adds 2.58% and 2.37% to the F-measure on the source and the target domains, respectively.


"Corolla" is not a common word and thus this was probably one of the only alternative senses.

we believe Google Adwords has already implemented some form of sense disambiguation for frequently-searched key phrases; it seems that frequently-searched negative senses for ads are already filtered out.


12 of the 50 key phrase pairs triggered ads from our campaign and 20 of the 50 had ads closely related to our campaign.

over-specification would simply involve selecting long enough and specific enough key phrases so that all ambiguity is removed.


A special heuristics is implemented in our system via the unification method provided by SProUT, in order to find the equivalent classes of persons and organizations.

avoiding the negative consequences of these factors and evaluating the quality of the TECH-WATCHTOOL system proper remains an open challenge.


We first run experiments using each of the three features TOPIC (T), SOURCE (S), AUTHOR (A) separately and then combined across the various N-GRAM and N-GRAM combinations described earlier.

this feature indicated the author of each the document to which the paragraph belongs.


While semantic networks have words in nodes and the arcs are semantic relations, wordnets are definitely more than that: they are:  monolingual dictionaries: they contain words with definitions for each of their senses;  multilingual dictionaries: via the Inter-Lingual Index, ILI, access from one language-specific network to all the others is facilitated; thus, it is possible to compare the organization of the lexical material of various languages, to find examples supporting the thesis of semantic specificity of languages, to introduce the multilingual dimension in various applications relying on wordnets;  thesauri: lexical information is organized in terms of word meanings, not word forms;  lexical ontologies: wordnets contain concepts lexicalizations from various domains and the relations between these concepts lexicalizations.

the high costs involved in terms of money and time prevent other teams to undertake a similar enterprise.


There, concatenation of inflectional morpheme for nouns and adjectives is performed not concerning the problem of the alternations in the root.

after applying the method of validation, we obtain correct words on the basis Figure 1: Cycle of the lexicon completion of language.


The DB population program produces a file that shows if words were inserted, word codes, and the result for each operation.

not all the derivatives correspond to the norms of human language.


In this work, we adapted each schema to represent an Arabic syntactic phenomenon (the simple one).

in fact, coordination interacts with different others phenomenon.


To analyze a simple phrase, we have to unroll the "parse" menu and choose the "parse input" order.

the study on Arabic language showed that coordination is one of particular structures.


Still, visualization of coreference information is somewhat rare.

in many cases, coreference is resolved using supervised machine learning methods.


Tokenisation: determine token (words, punctuation symbols) boundaries in sentences.

the corpus contains both external and intrinsic plagiarism cases, that is, cases where plagiarism is to be identified within the actual suspicious document, without referring to a source document.


For the experiment with lexical generalisation, functional words (stop words) were removed and all remaining (content) words were generalised using their WordNet synsets, that is, groups of synonym words.

for practical reasons, in this paper we selected a subset of the PAN corpus: the first 1,000 suspicious documents, along with all 11,147 source documents.


Reducing this variability should help Web-oriented QA.

for all the possible configurations of parameters, the system provides results for the complete QA chain.


While a summary, the most condensed form of a text, has to give an outline of the text contents that respects the text structure, a title indicates the treated subject in the text without revealing all the content (Wang et al., 2009).

the variation of λ between 0 and 1 determines the value adapted to the corpus.


The electronic dictionary that has been chosen is WordNet (Miller, 1995), as has already been mentioned.

in supervised approaches, there is an adequate number of labelled data to train the model from scratch, on the new domain.


In a sense, both approaches exploit information that can be characterised as "text relatedness" (or "feature relatedness"), as both "is-a" relations and correlation can be viewed as a relatedness measure between features.

the improvement was significantly better for case 3, while case 2 performed worse than the other two methods.


Expanding and duplicating vectors: this case is very similar to the previous one regarding dimensionality: the dimensionality also increases, identical to the previous case.

after our approach expands the feature space with features from the target domain, 2289 posts of the target domain contained at least one feature from the  (Shi, Fan and Ren, 2008) are also shown for comparison purposes (evaluated on different data partitioning).


The OCA corpus is composed of Arabic reviews obtained from specialized Arabic web pages related to movies and films.

also, another part of this project was funded by agencia Española de Cooperación Internacional para el Desarrollo MaEC-aECID.


In order to compare the experiment for Arabic and English, we have translated OCA into English using an automatic Machine Translation (MT) tool freely available.

the experiments carried out with the English Version of OCA (EVOCA) show that, although we lost precision in the translation, the results are comparable to other works using English texts.


With respect to closed categories most of the errors were due to the fact that units or symbols had not been defined in the hierarchy.

the tYPE and tEXt are credited independently, regardless if one of them is incorrect (Nadeau and Sekine, 2007).


Firstly, the properties associated with units or symbols and boundaries of the numerical entities are tagged.

the system was not able to identify and classify these entities correctly.


The first annotator in both English and Italian languages and the second one in both English and Czech languages.

if the goal is to detect sentiment expressed towards entities, the aggregated sentiment of the articles, in which the entity appears, need not to correspond to opinions expressed towards the entity.


agreement on the contrary, had clear indicators: 'I agree with', 'have to agree'.

we proposed to build discourse structure using RST and based on empirical analysis, to determine which types of discourse structures are leading to final consensus.


MADA  Both sets of experiments show that the amount of morphological and morpho-syntactic information present in the POS tagset has an influence on the difficulty of the POS tagging step, even though the connection is not always a direct one.

for the AMIRA-SPLIT, we follow the procedure by Diab et al.


DEV and TEST (each making up 10% of ATB3V3.1) respectively.

if ASMA is used as a preprocessing system for upstream modules, it is necessary to choose the tagset with regard to the upstream task.


posts) were classified as positive or negative stance with F-score 39%−67% (Somasundaran & Wiebe, 2009); when those posts were enriched with preferences learned from the Web, F-score increased to 53%−75%.

opinionated Sentence: I don't think you will find anyone who this level of amplification is undamaging, but the option is to not hear.


In case of noun the root word is the singular form of the plural noun, e.g., bottles becomes bottle, etc.

the data had an inheritably high major class baseline of Accuracy = 70% and F-score = 57%.


Further sub-categorization can be found in (Ghoul, 2011).

statistical and machine learning approaches generally require a large amount of manually annotated training data.


Therefore, the final patterns set (P) is filtered every time a new pattern is added to it.

for example, the short pattern "Dr./NN <Per-sonName >" might successfully match more NEs in the text than the long pattern illustrated in figure 3.


The methodology was applied without any major modifications.

sentence detection was applied to the corpora.


These annotated documents are then accessed via browser-based clients which essentially look like traditional e-book reading environments but with a much richer set of user accessible functionality.

our system has been implemented save for a couple of features and we are now in the process of planning an intrinsic evaluation followed by a deployment to have it be used to gauge if student users find it effective.


On the client side, the presentation layer is responsible for (i) keeping track of the user status and the opened documents, (ii) displaying the opened documents (iii) handling user-interactions, and (iv) sending queries to the server.

our system architecture is language-independent; adopting new languages is a fairly easy process as long as the relevant annotation tools and their UIMA interfaces are available.


We now turn to the experiments with enforced agreement based on a dyad of parsers.

miceli Barone and Attardi (2012) perform domain adaptation for dependency parsing using unannotated data.


Notably, the distribution of these labels in the Stanford parses as well as in the parser combination parses is similar to that of the gold standard.

domain adaptation can be divided into two different scenarios: one where a small set of annotated data from the target domain is available, and one where no annotated target data is available.


• We propose an effective method to refine/rank extracted tuples and patterns without human supervision.

the tuple, bought(Google, Youtube) can be extracted with high confidence.


Tables 10 and 11 present results for the both sub-forums.

they used character-based and word-based PPM.


The algorithm outperformed NB and KNN on both the forums.

given that individuals may be reluctant to share personal health information on online forums, they may choose to post anonymously.


the word "Govt" is normalised to government, which is then tagged correctly as NN, instead of NNP.


Our training data was extracted from the Romanian Speech Synthesis Corpus (RSS) (Stan et al., 2011) and it is comprised of a small number of words (8K).

this reduced our model size about 5 times.


Another issue was the slight discrepancy between the set of labels from the manual annotation and the models.

finally, for evaluation purposes, a gold standard "corpus" was required, in which dependencies relationships containing a polarized words and a contextual valence shifter have been annotated.


AP(<ADJ:loin de>,<ADV:>)) and often impact a phrase or a whole sentence, not directly a lexical item.

since, there was no such corpus available, we randomly selected 500 sentences from the whole NO-3 http://fr.nomao.com/ MAO corpus and discarded them from this corpus, that was therefore considered as the training corpus.


Limited to noun phrases, (Campbell et al., 1999) applies pattern-based rules and combines them with UMLS concepts to acquire new and semantically classified terminology.

semantic classifications such as pathological information are particularly missing so far.


If sentences are misclassified as 'pathological' (although they describe non-pathological findings), this is a minor issue.

for learning, we adapt the standard probabilistic CKY parsing algorithm.


It should be noted that using previous and following morphs in English is not very beneficial due to the simple morphology of the language.

furthermore, morphs that are attached to an unvoiced consonant ending word are also harmonised and the first morph letters become also an unvoiced consonant (i.e.


Furthermore, the semantic class that a preposition can take is language specific.

the improvement given by multilingual evidence is rather small.


For example, the article for the entity Turing that refers to the "English computer scientist" has the unique identifier ALAN TURING, whereas the article on Turing with the "stream cipher" meaning has the unique identifier TURING (CIPHER).

the pairwise Pearson correlation between the two annotators was measured at 0.77 and 0.83 for English and Spanish respectively, which represents a high agreement.


The corpus includes misspellings from young children as well as extremely poor spellers subject to spelling tests way beyond their ability.

he defines the problem as 'a single word w and a large set of words W , quickly deciding which of the words in W most closely resembles w measured by some metric of similarity, such as minimum edit distance' and points out that finding the closest match between w and a large list of words, is an extremely demanding task.


from native language identification (see among the others Koppel et al.

for what concerns morpho-syntactic features such as Part-of-Speech distribution, literary texts show a higher occurrence of pronouns and verbs, two features which are more common in conversation than in written language varieties (Biber and Conrad, 2009).


For what concerns morpho-syntactic features such as Part-of-Speech distribution, literary texts show a higher occurrence of pronouns and verbs, two features which are more common in conversation than in written language varieties (Biber and Conrad, 2009).

achieved results are evaluated in terms of overall accuracy, Precision, Recall and F-measure.


Letλ β be the maximizer of the expected F β measureF β .

we used 400 of the samples for training and 200 for testing.


The averaged perceptron approach takes, instead of the final value of the parameter vector θ, its averageθ over all the iterations.

closest to our application is the application of mined morphological paradigms in (Durrett and DeNero, 2013), the morphological unit segmentation in (chang and chang, 2012) and the Finnish morphological generation for machine translation in (clifton and Sarkar, 2010).


An example is that of the verb a putea (to be able to), whose stem vowel u transforms into o and oa, forming a singleton alternation pattern.

the validation scores are computed using tenfold cross-validation over the training set, and the best hyperparameters, in terms of word-level accuracy, for each learning method, are presented in table 4.


This result suggests that using a reduced tag set yields a more informative performance estimation for some specific task because irrelevant tagging errors are not taken into account, but it does not necessarily lead to increased performance  Table 4: Cross-genre F 1 performance of the tagging models; main diagonal represents performance using 10-fold cross-validation for the relevant original tags.

when tackling a specific syntactic phenomenon, only a subset of signs and tags may be involved.


Excellent performance (F 1 > 95%) is noted for the complementiser that and whsigns such as who, when or which.

signs bounding subordinate constituents are often not matched pairs.


1 Acronyms are formed to speed up and ease communication, mainly to create words for concepts frequently used or dif-ficult to describe.

the third column summarises how many news articles need to be analysed to find a new (i.e.


The last column provides the ratio between the number of LFs for the same SF, considering all SFs.

not much work exists for languages other than English.


In this method, all the nodes of the path (the path from event nodes to their common parent) and the ones among this path would be designated as the desired portion of tree.

the task of temporal relation classification in Persian is more complicated than in other languages such as English.


In original SSK, an alphabet letter is assumed as a comparing unit that can be expanded to sub-string by increasing the K value.

high G-ExcEva of the signal class is an evidence of this improvement.


SSK Kernel: SSK was initially proposed for estimating a similarity measure between sequences (Lodhi et.

a simple mapping method that relates a node label to an individual ASCII character can be used for the SSK adaptation.


We argue that applying a modelling approach originally designed for lexicons to a wider range of language phenomena brings a new perspective to the relationship between theory-based and empirically-based approaches to language processing.

this fragment defines a node called DOG with two path equations, specifying that the (syntactic) category is noun, and the (morphological) form is dog.


It defines <pos> to be the value "<table "<prev pos>" "<prev prev pos>" >".

this definition presupposes a feature <vstart> which returns true for words that start with a vowel, false otherwise 3 .


The user was encouraged to reflect on why the system might have misidentified his introduction.

the primary concern of our system is to promote self-regulated learning, self-knowledge, and metacognition.


Each phrase generalization is based on up to 12 string comparisons, taking an average size of phrase as 5 words.

hence we define the operation of generalization on a pair of PT as finding the maximal common sub-thickets based on generalizing phrases from two paragraphs of text.


Taking into account a separate generalization of noun and verb phrases, this average case consists of 2* 45*45 generalizations, followed by the subsumption checks.

of generalizations of two RST relations of the same sort (evidence) we obtain Iran nuclear NNP -RST-evidencefabricate by USA.


As a candidate generator we use a variant of the algorithm presented in (Gerdjikov et al., 2013).

we can use a gradient method to find a local extremum of F .


Polanyi and Zaenen (2006) investigate the usage of contextual valence shifters and discourse connectives inside a text.

there are some open issues related to this research area, commonly known as Opinion Mining, which can be summarized as follows: (1) Opinions are potentially ambiguous, and (2) Contextual interpretation of polarity is hard to achieve.


For example, there are plenty of instances where beardedness or wearing glasses are distinguishing attributes by themselves, but this is not the case for most other attributes (e.g., wearing a tie).

as a novelty, we incorporate features of the role of the underlying task, object identification, into these property specifications; these features are inherently domain-independent.


Thus, we assume that people not only choose attributes on the basis of some intrinsic properties, such as salience, but also on the basis of their contribution to the identification of the intended referent.

we first discuss previous work, then we motivate our approach.


Choices in this procedure can be made more specific by the corpus frequencies, thus incorporating some element of the majority of approaches to the GRE challenge (such as (Bohnet 2008) and (Kelleher, McNamee 2008)).


The multilingual lexicon was then compared against the existing WordNets: PWN (Fellbaum, 1998) for English; BalkaNet (Tufis, 2000) for Czech, Romanian and Bulgarian.

further submeanings were also defined but for the purposes of this exercise we assumed them to be part of the main meanings.


We used the Most Frequent Sense (MFS) as the first baseline for this comparison.

on occasions, a combination of translations would correspond to more than one sense of the word.


We therefore use an existing multilingual lexical resource (Lefever, 2009) to develop a large, artificial parallel corpus containing semantically disambiguated polysemous words, and use it to calculate the maximum contribution that parallel corpora can make towards WPSD under ideal conditions, when all other processing steps are 100% accurate and therefore do not introduce any noise to the process.

the quality of proto-synsets is only as good as the quality of word alignment, not worse.


For the best combination of kernels, they obtained F-measure equal to 77.62%.

we start from the first AgP 0 head to the left, then we proceed to the right, jumping from AgP i head to the closest AgP i+1 head to the right.


Each of written operators refers to one semantic relation.


This type of analysis, outputting stems and appended morphemes aims to identify some kind of border between the different morphemes.

there is no need for length adjustment to these ratios since we are considering only three letter roots.


Given this degree of error in tagging, a parser trained with similar noise in POS tags may outperform one which is trained on gold tags.

16 000 sen-tences from weblogs, newsgroups, emails, reviews, and question-answers.


Next, we compared the effect of training and testing within sub-genres and saw that features such as sentence length have a strong effect.

note that the difference in the number of words in the training set across these three methods is minimal: they vary only by 41 words.


The raw sentence-internal co-occurrence counts from the original matrix have been transformed into Local Mutual Information scores (Baroni and Zamparelli, 2010;Evert, 2005).

baroni and Zamparelli (2010) suggest treating adjectives as distributional functions that map between semantic vectors representing nouns to ones representing AN combinations.


As an example, "Peter drives a red" can be analyzed as Su bj Det Ad j Obja using structural prediction.

since these transformations all work independently, they have been parallelized in the same manner as the initialization.


Each time a new word w is pushed to jwcdg, MaltPredictor forwards w together with its PoS-tag onto MaltParsers input queue and runs MaltParser's algorithm until a shift operations occurs.

since the PoS tags are needed and a PoS tagger needs lookahead as well to achieve good accuracy, a lookahead of at least three words is needed for the whole taggerparser pipeline to achieve a high accuracy.


From semantic representations to surface phonologic representations, seven different levels of linguistic representation are supposed for each set of synonymous utterances.

both of them are based on directed labelled graph structures, and some research has been done towards using them to represent dependency structures and knowledge of the ECD (OWL in (Lefrançois and Gandon, 2011;boguslavsky, 2011), CGs at the conceptual level in (bohnet and Wanner, 2010)).


The set of unit node markers is denoted M ∩ and is the powerset 5 of M. If a unit node is marked by ∅, it is said to be generic, and the represented unit is unknown.

for a specific Lexical Unit L, (Mel'čuk, 2004, p.5) distinguishes considering L in language (i.e., in the lexicon), or in speech (i.e., in an utterance).


We use a slot filling system that has achieved highly competitive results (ranked top 2) at the KBP2012 evaluation as our baseline.

the effectiveness of this model has been demonstrated in the challenging Knowledge Base Population Slot Filling task, where a weighted voting system achieves 2.3% absolute improvement in F-measure score based on the confidence estimates.


Two annotators are trained to perform the annotation.

when parsing the context sentence, we replace each citation content with a <CITATION> symbol, in order to remove the contextual bias.


So, the best result (97.90 of BLEU and 0.0114 of TER) could be considered as an upper bound for proposed approach.

similar approaches are used by Oflazer (2008) for English to Turkish sMT, Luong et al.


Our proposed approach predicts each morphological feature independently.

morphological analysis is an important phase in the translation from or into such languages, because it reduces the sparseness of model.


it is positive in the food domain while it is negative in the negative domain), contributing to the need for domain adaptation.

in the all-in-one classifier experiments, the sentiment classifier is trained with 26 domain datasets while testing it with the 27 th domain.


(2007), the all-in-one exceeds the ensemble only in the DVD domain.

creating three versions of each feature in both the source and the target domains grows the feature space exponentially, which is prohibitive in a many-domain adaptation scenario such as ours which consists of a total of 27 domains.


We focus on the manual rule creation approach and discuss to what extent the existing tools can be used for this task.

we present a language independent formalism for rule creation and execution called wCCL Relation.


As long as there is a continuous sequence of EX-PAND relations linking the subsequent sentences, citations in those sentences can fill the ARG2 slot for St SLink.

some example instances of these CON-TRAsT/CORROBORATE relations are provided in Figure 1, the document containing the citations (represented as we or this study) contrasts with [ We use square brackets and numbers (IEEE style) to represent citations that are the object of this study (Figure 1).


the non-initial use of roughly still means something like approximately, but the connection with the previous sentence is no longer there, e.g., The odds were roughly 8 to 1.


However, they indeed play an important role in distinguishing some classes of verbs.

while "divide" and "open", which belong to unaccusative class, are clustered together into the object-drop class.


In other word, considering whole document as the context window is more helpful than considering just whole sentence for reranking the candidate.

in order to detect and correct this kind of error, context analysis of the text is crucial.


Let assume that w i is in j-th phrase of E, then, we can estimate | as follows: Equation 4is the same as phrasal translation model in phrasal SMT systems.

some statistical methods use Google Web 1T N-gram data set to detect and select the best correct word for a real-word error (Bassil & Alwani, 2012;Islam & Inkpen, 2009).


We propose and evaluate several strategies for making use of these multiple inputs: (a) select one of the datasets, (b) select the best input for each sentence, and (c) synthesize an input for each sentence by fusing the available inputs.

this is a very poor strategy here; however, below we will see that it is quite reliable if we make a choice based on length ratio.


The Stanford CoreNLP tool kit is used for tokenization, POS tagging (Toutanova et.

this paper proposed the automatic generation of Multiple Choice Questions(MCQs).


The previous two stages (sentence selection and keyword selection) are not domain specific in nature i.e.

automatic CQG has received a lot of research attention recently.


The existence of translationese has been discussed and more recently various methods (Koppel et al., 2011;Ilisei et al., 2010) for identifying translationese have been devised.

ìèìî, äîìîé, ïðîòèâ, íàïðîòèâ, îêîëî, äàëåêî, âèäíî, âîêðóã, ãîðàçäî, âîí, âåñåëî Observation: Sometimes the counter of the "the best" result and the counter of the "second best" result could have similar values but this was not the case for us.


Then for each n/2 < i ≤ n we create a set F i by adding consecutively one more word found at position i in the entire set F .

in order to inspect our results we used a hierarchical clustering method and draw conclusions based on the most frequent result.


Implementing just a few rules for fixing these, we achieved 97.84% token accuracy and 86.77% clause accuracy on the development set.

while a 10-million-word portion of their English newspaper corpus has less than 100,000 different word forms, a corpus of the same size for Finnish contains well over 800,000.


Moreover, there are much more different possible morphosyntactic tags in the case of agglutinating languages (corresponding to the different possible inflected forms) than in English (several thousand vs. a few dozen).

one such case from Hungarian is briefly discussed in (orosz and Novák, 2012).


We begin the analysis of the results by reporting the effectiveness of the subjectivity unigram classifiers in Table 1.

having built the unigram-based subjectivity and polarity classifiers in the first stage of the process, the sentence of each training document is classified in terms of its subjectivity and polarity.


This decision contrasts with the way that sentence-based sentiment analysis is utilized by Mao and Lebanon (2006) and the experiments presented in section 5 indicate that it typically results in increased accuracy.

at training time, it requires labeled data at all levels of analysis, which is a significant practical drawback.


A triple x, y, w from a KS K i suggests linking x to synsets including y.

the evaluation is based on wordnet reconstruction task proposed in : randomly selected words are removed from a wordnet and next the expansion algorithm is applied to reattach them.


There is no perfect recipe that allows identifying all good and sufficient mappings from all bad and incomplete mappings in a language independent fashion, however, the mapper allows users to decide whether non-mapped segments at the beginning or the end of terms should be allowed or prohibited.

given two lists of terms (in two different languages) the task of the term mapping system is to identify which terms from the source language contain translation equivalents in the target language.


Previous research suggests several possible vector representations for documents.

this might not be the case for cross-domain graphs.


The baseline stands for the performance of a linear-kernel SVM classifier trained on the source data.

in the field of sentiment analysis GB models have been employed for sentiment classification (Pang and Lee, 2004;Goldberg and Zhu, 2006;Wu et al., 2009), automatic building of sentiment lexicons (Hassan and Radev, 2010;Xu et al., 2010), cross-lingual sentiment analysis (Scheible et al., 2010) and social media analysis (Speriosu et al., 2011).


They proposed to reorder VS construction into SV order for SMT word alignment only.

table 3 compares the results, in term of BLEU scores, of the three experimental settings in 3 evaluations schemes, as follows: (a) Standard, which includes performing recasing and removing white space before punctuation, (b) Nopunct, in which punctuation is stripped and evaluation is performed on the lexical text only, and (c) Nopunctcase in which, in addition to removing punctuation, all words are lower-cased.


One the one hand, developing an Arabic-French machine translation system is not an easy task, although there is a vast amount of training data nowadays.

in this paper, we describe an ongoing effort to build our first Arabic-French phrasebased machine translation system using the Moses decoder among other linguistic tools.


This grammatically complete sentence carries a conjunction w, a future particle s, a verbal token yktbwn, and a feminine singular third person object pronoun hA.

dealing with the complexity and ambiguity of the source language plays a major role in boosting the efficiency of the translation system.


These adhoc models differ significantly from the models of how translation happens that are used during actual translation, which violates the basic machine learning assumption that the same model should be used during training and testing.

the LItG is then transformed into a full ItG whose parameters are again tuned to the training corpus.


Theoretically, proper names do not occur in a hypernym relation, since whether or not they would be considered correct is highly dependent on the context of the document: the hypernym pair (priest, John) can be correct in a text where John is in fact a priest, but if another non-priest John is referred to this pair would be incorrect.

correct hypernymhyponym pairs not occurring in the same clus-ters are erroneously eliminated by the filtering module.


To measure relatedness, most of the lexicon-based approaches rely on the structure of the lexicon, such as the semantic link path, depth (Leacock and Chodorow, 1998;Wu and Palmer, 1994), direction (Hirst and St-Onge, 1998), or type (Tsatsaronis et al., 2010).

here again, the results are reported in terms of accuracy.


The search algorithm will be executed as many times as necessary, so that all positive examples are covered, i.e., the set of positive examples is the empty set.

hence we are convinced that it is admissible to investigate the possibility of generalizing this information using a classification algorithm.


<myocardial infarction, heart attack>, <emesis, vomiting> etc.

"your doctor would call it") have the tendency to remain isolated.


This study is certainly more accurate wrt previous similar works that use few manually defined keywords (Althouse et al., 2011), such as flu and influenza.

2 "For the past 3 days I have had a stuffy, runny nose, congested chest, fever, sore ears and throat and burning eyes.


Systematic keyword analysis has shown that being able to trace both technical and naïve terminology produces a much larger body of evidence.

first, the algorithm depends on the availability of critical resources: web query logs are a kind of data which is not freely available.


we also provide in this article two distinct ways to combine them according to different purposes.


Unlike , our CONJ chunk only contains the conjunction token(s) and, as opposed to Paroubek et al.

every discrete HMM can be "transformed" into a CRF model defining exactly the same probability distribution (Sutton and McCallum, 2006;Tellier and Tommasi, 2011).


Future linguistic analysis will clarify the unexpected behavior of A61K.

definitions of "sublanguage" vary, but have some commonalities.


This could include indexing by semantic classes of named entities relevant to the domain of the patent, relationships between semantic classes of named entities, and the like.

genre and domain differences were measured by calculating the average sentence length, the type-token ratio and the hapax ratio.